Most of the previously cited actuator location/selection methods rely on linear analyses. In the current study, we aim at introducing a reinforcement-learning-based method, leveraging some ideas previously introduced in [1]} but this time to select actuators instead of sensors.
The present paper provides an in-depth study of a new sparsity-seeking algorithm for actuators, a question that has never been addressed in the context of flow control by RL to our knowledge. By selecting two test cases, each one being challenging in their own way, the paper gives a critical analysis of the algorithm. This allows a more general discussion about the difficulties encountered when one tries to use RL for automatic actuator placement. Therefore, the goal of the present paper is twofold: introducing the first RL algorithm that tackles the essential question of actuator placement for flow control, and identifying future challenges and research directions to pave the way for upcoming work on the topic.
Before describing the proposed action-sparsity-seeking algorithm, the reinforcement learning method used as a base to the algorithm is introduced in part . Both test cases are then described and results of the method are discussed in sections  and . At last, a discussion about the limitations of the proposed sparsity-promoting method is led.
